Below, I provide the slides for a number of lectures that I have recently or semi-recently given. The lectures are separated into two categories: “statistics & machine learning” and “naive discrimination learning”.

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**statistics & machine learning**[]

machine learning algorithms

An overview of machine learning algorithms in R, as applied to the MNIST database of handwritten digits.

Guest lecture in

Guest lecture in

*Advanced regression models*course taught by Harald Baayen, 2018.[]

comparison of machine learning algorithms

A short comparison of machine learning algorithms for the Kaggle competition “What’s cooking?”. In this competition, the task was to assign a cuisine to recipes on the basis of the ingredients used.

Presentation at my doctoral defense (of a topic orthogonal to my PhD thesis), 2015.

Presentation at my doctoral defense (of a topic orthogonal to my PhD thesis), 2015.

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gradient boosting machines

A brief introduction to gradient boosting machines in R.

Informal presentation at the University of Tübingen, 2014.

Informal presentation at the University of Tübingen, 2014.

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memory-based learning

An introduction to the principles of memory-based learning and examples of the application of memory-based learning in psycholinguistic research.

Guest lecture in

Guest lecture in

*Mathematics for Linguists*course taught by Harald Baayen, 2014.[]

vector semantics

An overview of various implementations of distributional semantic models.

Guest lecture in

Guest lecture in

*Cognitive models of language processing*course taught by Harald Baayen, 2016.[]

introduction to R

A very basic introduction to statistical analysis in R for researchers in the humanities. Package includes slides, as well as questions and answers for practical sessions.

Workshop at the Nijmegen Spring School for Humanities, 2013.

Workshop at the Nijmegen Spring School for Humanities, 2013.

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GAMM analysis of ERPs

An overview of the possibilities of generalized-additive mixed-effect models (GAMMs) in the context of event-related potentials (ERPs).

Welcome talk at the University of Tübingen, 2011.

Welcome talk at the University of Tübingen, 2011.

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survival analysis

Introduction to survival analysis and to piece-wise generalized additive models (PAMMs) for time-to-event analysis.

Guest lecture in

Guest lecture in

*Advanced regression models*course taught by Harald Baayen, 2018.[]

expectation and variance

Derivation of the concepts of expectation and variance in probability theory and examples of the application of both concepts.

Guest lecture in

Guest lecture in

*Mathematical Models: Statistics*course taught by Konstantin Sering, 2017.**naive discrimination learning**[]

introduction

An overview of the theory behind naive discrimination learning and examples of its application in psycholinguistic research.

General purpose slides, 2015.

General purpose slides, 2015.

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applications

A short summary of the work presented in my doctoral disseration “Experimental explorations of a discrimination learning approach to language processing”. This includes simulations of (behavioral patterns in) word naming data, eye fixation patterns and event-related potentials using naive discrimination learning networks.

Presentation at my doctoral defense, 2015.

Presentation at my doctoral defense, 2015.

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reading aloud (slides)

An extensive overview of the Naive Discriminative Reader Aloud (NDRa) model. The NDRa is an extension of the NDR model for reading aloud.

Presentation at the University of Trento, 2011.

Presentation at the University of Trento, 2011.